Machine Learning Engineer, Sponsored Product Demand Utilization

$129,300 - $223,600/Yr

Amazon - Palo Alto, CA

Full-time - Mid Level

Palo Alto, CA

Sporting Goods, Hobby, Musical Instrument, Book, and Miscellaneous Retailers

About the position

The Machine Learning Engineer position within the Sponsored Product Demand Utilization team at Amazon Advertising focuses on developing and optimizing machine learning infrastructure for both inference and training. The role involves hands-on collaboration with scientists to enhance the model lifecycle and productionization of ML systems, directly impacting customer experience and advertising effectiveness. The engineer will design, code, troubleshoot, and support high-volume, low-latency distributed systems, contributing to the delivery of relevant ads to customers and maximizing ROI for sellers.

Responsibilities

• Serve as a tech lead for defining innovative and cutting edge ML infrastructure for both inference and training
• Build POCs and infrastructure for deploying and supporting models in production
• Own A/B testing of experiments using this infrastructure
• Work closely with scientists across the org to understand requirements and impact opportunities
• Collaborate with product managers to identify opportunities for ML Infra to improve customer experience
• Stay updated on modern ML Infra and ML Ops technologies to provide value within the org
• Help attract and recruit technical talent, mentor engineers and scientists in the team

Requirements

• 3+ years of non-internship professional software development experience
• 3+ years of programming with at least one software programming language experience
• 3+ years of leading design or architecture of new and existing systems experience
• Experience as a mentor, tech lead or leading an engineering team
• Experience with common machine learning techniques such as pre-processing data, training, and evaluation
• Experience in building large-scale machine-learning MLOps infrastructure for inference, eval or other parts of the model lifecycle

Nice-to-haves

• 3+ years of full software development life cycle experience
• Industry experience in software development
• Experience with production machine learning systems
• Experience profiling and identifying performance bottlenecks on CPU, GPU or other accelerators
• Excellent distributed systems design experience
• Experience with ML libraries/frameworks such as PyTorch, JAX, Tensorflow, Keras
• Experience with MLOps tooling like MLFlow, Sagemaker, Kubeflow, DVC
• Experience with systems programming, and low level optimization in Rust, C++, C or other similar languages
• Coursework or thesis in machine learning, data mining, information retrieval, statistics or natural language processing
• Advanced knowledge of performance, scalability, enterprise system architecture, and engineering best practices

Benefits

• Medical insurance
• 401(k) plan
• Paid time off
• Employee discounts
• Tuition reimbursement
• Flexible work hours
• Professional development opportunities